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cutradenet

Module for GPU-Accelerated Kinetic Wealth Exchange Models on Complex Networks

https://github.com/qsanti/cutradenet

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org, sciencedirect.com, iop.org, zenodo.org
  • ○
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    Scientific vocabulary similarity
    Low similarity (13.3%) to scientific vocabulary

Keywords

complex-networks economics-models econophysics gpu-simulation wealth-distribution
Last synced: 6 months ago · JSON representation ·

Repository

Module for GPU-Accelerated Kinetic Wealth Exchange Models on Complex Networks

Basic Info
  • Host: GitHub
  • Owner: Qsanti
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 3.57 MB
Statistics
  • Stars: 7
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 2
Topics
complex-networks economics-models econophysics gpu-simulation wealth-distribution
Created over 3 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.md


cuTradeNet library provides classes to easily create & run kinetic wealth exchange models on complex networks.

Leads the user to set one (or ensemble) of complex networks as a contact structure agents use to trade about. The following wealth exchange models were implemented: * Yard-sale model * Merger-Spinoff model * Dragulescu and Yakovenko * Constant model * Chatterjee, Chakrabarti and Manna * "All in" model

It is written in Python and uses Cuda module from Numba package to accelerate the simulation runnin in GPU, paralelizing some transaccions in the same graph and paralelizing runs in multiple graphs, leading to easier & faster averaging of system properties. It's completely abstracted from the CUDA knowledge for the user, so you can use it as a regular Python library.

How to use

There is a Demo notebook in the repository that can be tryed in it's Google Colab version too (you can use the package there if you don't have a NVIDIA gpu).

There is also a General explanation of Kinetic Wealth Exchange Models used.

How to install

You can install it from PyPi with the following command: bash pip install cuTradeNet

Repository&Questions

The repository is in GitHub, and you can ask questions or contact us in the Discussions section.

CUDA dependencies

In order to use this library in your personal computer you should have a CUDA capable gpu and download the CUDA Toolkit for your OS. If you don't fulfill this requirementes you can always use it in the cloud. Don't hesitate to contact us to get help!

DOI

Owner

  • Name: Santi Cuevas
  • Login: Qsanti
  • Kind: user

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 0.1.1
title: cuTradeNet
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - family-names: Cuevas
    given-names: Santiago
    email: san.cuevas@protonmail.com
    affiliation: Instituto Balseiro
identifiers:
  - type: doi
    value: 10.5281/zenodo.7336541
    description: "Module for GPU-Accelerated Kinetic Wealth Exchange Models on Complex Networks"
abstract: >-
  cuTradeNet is a library that provides classes to
  easily create & run kinetic wealth exchange models
  on complex networks.
keywords:
  - economics-models
  - econophysics
  - wealth-distribution
  - 'gpu-simulation '
  - complex-networks
license: MIT
doi: 10.5281/zenodo.7336541
repository-code: "https://github.com/Qsanti/cuTradeNet"
version: 0.1.0
date-released: '2022-11-30'

GitHub Events

Total
Last Year

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 89
  • Total Committers: 2
  • Avg Commits per committer: 44.5
  • Development Distribution Score (DDS): 0.281
Top Committers
Name Email Commits
santiQ s****c@l****r 64
Santi Cuevas 4****i@u****m 25
Committer Domains (Top 20 + Academic)
live.com.ar: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 16 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 6
  • Total maintainers: 1
pypi.org: cutradenet

GPU-Accelerated Kinetic Wealth Exchange Models on Complex Networks

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 16 Last month
Rankings
Dependent packages count: 6.6%
Stargazers count: 21.8%
Average: 23.5%
Downloads: 27.8%
Forks count: 30.5%
Dependent repos count: 30.6%
Maintainers (1)
santiQ
Last synced: 6 months ago